Practical Generative Engine Optimization: Use AI Search Data to Win More Visibility

AI search engines don’t return ten blue links. ChatGPT, Gemini, Perplexity, Copilot, and Grok generate a synthesized paragraph in response to a question. That paragraph might mention your brand, link to your website, or ignore you entirely.

Generative Engine Optimization (GEO) is the practice of making sure AI engines mention you, cite you, and say good things when your topic comes up. This guide teaches you how to do it, step by step, using real AI search data from Superlines as the running example.

You will learn how to:

Assess where your brand stands across AI platforms
Find the specific gaps where competitors are winning and you are not
Discover the hidden search queries that AI models use behind the scenes
Turn every insight into a concrete content action
Automate the ongoing optimization process with an AI agent

How AI search visibility works

Before optimizing, you need to understand what you are optimizing for. Superlines tracks four core metrics:

Metric	What it measures	Why it matters
Brand Visibility (BV)	% of AI responses that mention your brand	Are AI engines aware of you?
Citation Rate (CR)	% of AI responses that link to your website	Do AI engines trust you enough to cite?
Sentiment	Positive, neutral, or negative language when you are mentioned	What are AI engines saying about you?
Average Position	How early in the response your brand appears	Are you the first recommendation or an afterthought?

These metrics are fundamentally different from Google rankings. In traditional search, you optimize a page to rank for a keyword. In AI search, you optimize your entire brand presence so that AI models choose to include you when synthesizing an answer.

The Superlines platform sends hundreds of tracked prompts into AI engines and records every response. This gives you a map of where your brand is visible, where it is invisible, and what content you need to change that.

Step 1: Assess your brand’s current position

Goal: Get a clear picture of how visible your brand is across AI search, and identify immediate red flags.

Open your Superlines dashboard and look at the four overview metrics. Here is what a real assessment looks like using Superlines’ own data:

Metric	Current value	Trend	What it means
Brand Visibility	1.7%	+0.3% ↑	Out of ~10,800 AI responses tracked, ~181 mention Superlines
Citation Rate	5.0%	-0.4% ↓	~537 responses include a link to superlines.io
Sentiment	64% positive	-13.4% ↓	Positive, but the trend is a warning flag
Average Position	12.0	—	Superlines appears ~12th in the response, not near the top

How to interpret these numbers

Brand Visibility at 1.7% sounds low, but context matters. In this category, the market leader (Semrush) sits at 24.9%. For a newer product, 1.7% with positive momentum means AI models are beginning to pick up your content. If your BV is below 0.5% and flat, that is a foundational problem — the AI models don’t have enough quality content about your brand to draw from.

Citation Rate falling while Brand Visibility holds. This is the most actionable signal here. It means AI engines are mentioning Superlines in text but pulling back on actually linking to its pages. This pattern typically means the content exists but is not trusted as a primary source. The fix is specific: find which pages are being cited (Step 4 covers this) and strengthen them with richer, more authoritative content.

Sentiment declining is a warning, not a crisis. At 64% positive with zero negative mentions, the brand perception is healthy. But the trend downward often means competitor content is getting more prominent in AI training data, which shifts the narrative. The response is to publish proactive comparison content and third-party reviews that reinforce your positioning.

Example: What to conclude from this assessment

Looking at the Superlines data, the assessment is:

“Our brand is gaining mentions (+0.3% BV), but AI models are becoming less willing to link to us (-0.4% CR). Our sentiment is positive but declining. Priority: improve the quality of pages that AI currently cites, and publish content that strengthens our narrative before competitors do.”

That is a specific, actionable conclusion — not a vague “we need to do better.”

Step 2: Find which AI platforms matter for your brand

Goal: Stop trying to optimize everywhere at once. Focus where the opportunity is largest.

Every AI engine pulls from different training data and has different citation patterns. Your brand visibility will vary wildly across platforms. Here is what Superlines’ platform breakdown looks like:

Platform	Brand Visibility	What this means
Grok	7.5%	Strong presence — Grok is citing Superlines regularly
Google AI Mode	2.5%	Moderate presence, likely pulling from web content
Google AI Overviews	2.1%	Similar to AI Mode, drawn from indexed pages
Copilot	1.9%	Some presence — Copilot uses Bing’s index
Perplexity	1.0%	Low but present — Perplexity cites recent, structured content
Gemini	0.4%	Near-invisible
ChatGPT	0.1%	Effectively invisible
Claude, DeepSeek, Mistral	0.0%	Not mentioned at all

How to use this data

Do not try to fix every platform. Pick one or two based on strategic importance:

Protect where you are strong. Superlines has 7.5% visibility on Grok. Find the prompts and content driving that visibility and keep them updated. Losing an existing position is harder to recover from than gaining a new one.
Attack where the audience is. ChatGPT handles the largest share of AI search queries, and Superlines is at 0.1%. That is the biggest opportunity. But improving ChatGPT visibility requires a specific approach: ChatGPT tends to cite high-authority roundup articles, comparison pages, and well-structured content on high-DR domains.
Match platform to content type. Perplexity cites recent content with clear headings and direct answers. Gemini pulls heavily from Google’s own index. Copilot draws from Bing. Each platform responds to different content signals, so your strategy for each should differ.

Example: Choosing platform priorities

Based on the Superlines data, a reasonable prioritization would be:

Priority 1: ChatGPT — Largest audience, nearly zero visibility. Requires getting featured in authoritative third-party comparison articles. Priority 2: Perplexity — Active audience of researchers and buyers, and Perplexity rewards fresh, well-structured content that Superlines can publish directly. Priority 3: Protect Grok — Already strong at 7.5%, ensure this doesn’t decay.

Step 3: Discover what AI models are actually searching for

Goal: Find the hidden queries that AI engines generate when answering user questions, so you can create content that appears in those searches.

This is the most technically unique and underused capability in Superlines. When a user asks an AI engine a question, the AI doesn’t just answer from memory. Modern AI systems perform real-time web searches to ground their responses. They generate their own internal search queries — called fan-out queries — to find current information before composing an answer.

The Fan-Out Queries section in Superlines shows you exactly what those AI-generated search queries are, which URLs are winning citations for each one, and whether your site appears in the results.

Example: Reading fan-out query data

Here is a sample of fan-out queries from Superlines’ tracked prompts:

Fan-out query	Citations	Top cited URL	Your URL	Your ranking
ChatGPT vs competitors	30	Visual Capitalist	—	Not ranked
alternative to Morningscore…	30	Sell.ms	superlines.io	#10
features pricing	20	AUQ.io	—	Not ranked
rank tracking	26	AIClicks	—	Not ranked
generative engine optimization	14	PR Newswire	—	Not ranked

What this tells you — and what to do about it

Out of five high-citation fan-out queries, Superlines appears in only one — and at position #10. This is the critical insight: if the AI can’t find your content when it searches the web, it can’t cite you. You are blocked at the source.

Each fan-out query where you have no URL is a content brief. Here is how to translate the data into action:

Gap	Content action
”ChatGPT vs competitors” — no URL	Publish a comprehensive ChatGPT comparison page or get featured in existing comparison articles on high-authority domains
”rank tracking” — no URL	Create a page targeting “AI rank tracking tools” with Superlines positioned prominently
”generative engine optimization” — no URL	Publish the definitive guide to GEO (like the article you are reading now)
“alternative to Morningscore…” — rank #10	Improve the existing comparison page: add structured data, fresher benchmarks, and build backlinks to move from #10 to top 3

Fan-out queries are the closest thing AI search has to keywords. Treat this table as your primary content calendar input.

Step 4: Identify which competitor content is winning — and why

Goal: Understand specifically which pages the AI models cite most, and reverse-engineer what makes them successful.

The Top Domains & URLs section shows every URL that appears in AI responses to your tracked prompts, ranked by citations. This is competitive intelligence in its purest form.

Example: Competitive citation analysis

Looking at the top-cited pages across all tracked prompts:

Rank	URL	Citations	Change	Domain Rating
1	seranking.com/blog/chatgpt-rank-tracking-tools-2026	1,541	NEW	DR 74
2	rankability.com/blog/best-ai-search-visibility-tracking-tools	1,319	+1613%	DR 45
3	seranking.com/blog/best-ai-visibility-tools	975	+98%	DR 74

Three patterns to notice

1. Comparison articles dominate. The top-cited pages are “best tools” roundups on third-party domains. AI engines, when asked about a category, reach for comprehensive comparison content. If you are not featured in these articles, you are missing the highest-value citation opportunities.

2. Freshness gets rewarded fast. The Rankability page grew +1,613% in citations — meaning someone recently published or updated a comparison article and AI engines immediately started citing it heavily. AI search rewards recency more aggressively than traditional search.

3. Domain authority matters but is not everything. Rankability (DR 45) is outperforming many higher-DR sites because its content is specifically structured for AI citability — clear headings, direct answers, comprehensive coverage.

Turn this into action

For your own URLs: Filter to “My URLs” and list every page currently being cited. These are your highest-value content assets. Protect them like you would a page ranking #1 in Google — keep them updated, expand them, and ensure they contain the most current information.

For competitor URLs: For each fast-growing competitor URL, analyze what makes it successful:

Is it a comparison/roundup article? Create or get featured in equivalent content.
Is it freshly published? Update your own competing pages with newer data.
Does it have a clear, structured format? Match or exceed that structure.

If you have the AEO Agent set up, this is where Phase 2 (Competitive Deep Dive) comes in — the agent’s Researcher automatically scrapes the top competitor URLs, analyzes their content structure, and identifies what makes them effective for AI citations.

Step 5: Find your highest-leverage opportunities

Goal: Instead of guessing what to work on, use data to identify the specific prompts and topics where effort will produce the biggest return.

The Superlines Opportunities section surfaces four types of alerts, each pointing to a different action:

Visibility Opportunities — Low visibility, high potential

Example: The prompt “alternative to profound ai” shows 5% current visibility with +95% potential gain.

Users searching for alternatives to a competitor are in active buying mode. A dedicated comparison page targeting this prompt — with honest pros and cons, pricing comparisons, and migration guidance — can capture a significant share of AI responses. This is the highest-intent content you can create.

Competitor Threats — A competitor dominates, you are invisible

Example: “Best dashboards for AI search visibility” shows Semrush at 53% visibility and Superlines at 0%.

A 53% gap on a category-defining prompt means Superlines needs a page that AI engines recognize as authoritative on this exact topic. The content brief is clear: publish a comprehensive guide to AI search visibility dashboards, featuring Superlines’ capabilities prominently, with enough depth and external validation that AI models treat it as a credible source.

Citation Opportunities — You already rank, protect and strengthen

Example: superlines.io/articles/best-chatgpt-tracking-t… is at position #1 for “MorningScore ChatGPT tracker alternative?”

You already have the winning content asset. Don’t create something new — protect what you have. Update this page regularly, add fresh data, and build additional backlinks pointing to it. Decay is the biggest risk for existing citation positions.

Query Opportunities — High-volume queries where you have no content

Example: “Tracker ChatGPT” has 590 searches/month and Superlines has no ranking URL.

This is a confirmed content gap. A targeted, well-structured page optimized for this exact query can earn a citation position. When creating this content, follow the AI-citable content principles covered in Step 6.

Prioritizing opportunities

Not all opportunities are equal. Use this framework:

Priority	Signal	Action timeline
Highest	Citation opportunity — you already rank	This week: update and strengthen the page
High	Competitor threat on category-defining prompt	This week: start a content brief
Medium	Visibility opportunity on buying-intent prompt	Next 2 weeks: create comparison content
Lower	Query opportunity on informational prompt	Add to monthly content calendar

Step 6: Create content that AI engines actually cite

Goal: Learn the specific content patterns that make AI models more likely to mention and link to your pages.

AI engines don’t cite content randomly. They follow patterns, and understanding those patterns is the difference between content that gets referenced and content that gets ignored.

Content structure rules for AI citability

Based on analyzing hundreds of top-cited pages across AI platforms, here are the patterns that work:

1. Lead with a direct answer. AI engines are looking for a clear answer to synthesize. If your page buries the answer under 500 words of introduction, the AI will find a competitor page that answers directly.

# What Is Generative Engine Optimization?

Generative Engine Optimization (GEO) is the process of optimizing
your brand's presence in AI-generated search results from ChatGPT,
Gemini, Perplexity, and similar platforms. Unlike traditional SEO
which targets search engine rankings, GEO focuses on getting
mentioned, cited, and recommended in AI-synthesized responses.

The first paragraph answers the question. Everything after that expands on the answer.

2. Use headings that match search queries. AI models search the web using natural language queries (the fan-out queries from Step 3). Your H2 and H3 headings should match those queries directly.

## How does AI search visibility differ from traditional SEO?
## Which AI platforms are most important for brand visibility?
## How to track your brand mentions in ChatGPT and Gemini

Each heading is a question that an AI model might search for. When the heading matches the query, the AI is more likely to cite the content beneath it.

3. Include external data and statistics. AI engines view pages with external data citations as more authoritative. Include at least 3 statistics from third-party sources per article.

According to a 2025 Gartner study, 65% of B2B buyers now use
AI search tools during their evaluation process
([source](https://www.gartner.com/...)).

Pages that cite data are cited more than pages that make unsupported claims.

4. Structure comparisons as tables. When AI models need to compare options, they look for structured data. Tables are easier for AI to parse and reference than paragraph text.

| Tool | AI Platforms Tracked | Citation Tracking | Starting Price |
|------|---------------------|-------------------|----------------|
| Superlines | 10+ (ChatGPT, Gemini, etc.) | Yes | €89/mo |
| Competitor A | 5 | Limited | $149/mo |
| Competitor B | 3 | No | $99/mo |

5. Add FAQ sections. FAQ content maps directly to the question-answer format that AI engines use. Aim for 5 questions per article, each answered in 2-3 sentences.

How the AEO Agent automates content creation

If you’re creating or updating content manually, the rules above will guide you. But if you want to scale the process, the Superlines AEO Agent automates the entire content intelligence and writing workflow.

The agent runs a 7-phase pipeline that maps directly to the steps in this guide:

Step in this guide          AEO Agent phase
─────────────────────       ──────────────────────────────
Step 1: Assess position  →  Phase 1: Intelligence Gathering
Step 4: Analyze competitors →  Phase 2: Competitive Deep Dive
Protect existing content  →  Phase 3: Content Health Audit
Keep data accurate        →  Phase 4: Fact-Check
Find new angles          →  Phase 5: Industry Insights
Embed data in content    →  Phase 6: Data Storytelling
Create + optimize pages  →  Phase 7: Content Actions

Here is what each phase does in practice:

Phase 1 — Intelligence Gathering. The Analyst agent calls Superlines’ analytics tools to pull brand visibility metrics, citation rates, competitive gaps, and content opportunities. This is the automated version of Steps 1-3 in this guide.

Phase 2 — Competitive Deep Dive. The Researcher agent scrapes the top competitor URLs that are winning AI citations (the same URLs you found in Step 4) and analyzes their content structure, topics, and data citations.

Phase 3 — Content Health Audit. The Content Manager inventories all published articles and flags any that are older than 6 months, contain outdated year references, or are missing key structural elements.

Phase 4 — Fact-Check. The agent extracts every claim in your content — pricing, statistics, feature counts, dates — and verifies them against live sources. Outdated claims are flagged for correction.

Phase 5 — Industry Insights. The Researcher searches for trending topics in AI search optimization, GEO, and AEO across the web and Reddit, identifying new content ideas and data points to embed in articles.

Phase 6 — Data Storytelling. The Analyst mines your Superlines analytics data for compelling insights — performance by LLM platform, sentiment trends, brand mention patterns — that can be embedded directly into articles as proprietary data.

Phase 7 — Content Actions. Based on everything gathered in phases 1-6, the Content Manager creates new article drafts, updates outdated content, and fixes incorrect facts in your CMS. All new content is created as a draft for human review — the agent never auto-publishes.

The agent runs daily, producing a report and CMS updates each time. Over weeks, this creates a compounding effect: every content gap gets filled, every outdated fact gets corrected, and every new competitive signal gets acted on.

To set up the agent, follow either:

Beginner setup guide — for non-technical users, walks through every click
Advanced build guide — for developers who want to build it from scratch

Step 7: Monitor mentions to understand your brand narrative

Goal: Know exactly how AI engines describe your brand, and correct the narrative when it drifts.

The Latest Mentions section shows the actual prompts where an AI engine mentioned your brand, which engine generated the mention, and when it happened.

Example: Reading the mention context

Recent Superlines mentions:

Prompt	Platform	Date
”best ai mode rank tracker tool”	Copilot	Feb 18
”ai search visibility tools comparison”	Copilot	Feb 18
”best copilot rank tracking”	Grok	Feb 18
”alternative to morningscore chatgpt tracker”	Grok	Feb 18

What to do with this data

Search for the actual AI response. For each mention, go to that AI platform and ask the same question. Read how your brand is described. The language AI uses reflects the dominant narrative in the content it was trained on. If the description is vague, generic, or positions you as an afterthought, that is a direct signal that your content is not differentiating enough.

Track narrative patterns. If AI consistently describes you as “one of many tools” rather than “the leading platform for X,” you have a positioning problem in your content, not a visibility problem. The fix is to publish more specific, differentiating content about what makes your approach unique.

Use as a weekly pulse check. Compare this week’s mentions to last week’s. New prompts appearing in mentions mean your reach is expanding. Prompts dropping out signal content decay on the pages being cited.

Step 8: Translate visibility into business impact

Goal: Connect AI search metrics to numbers that leadership cares about.

The Funnel Estimation in Superlines converts your AI visibility data into a traffic model:

Funnel stage	Value	% of total
AI Search Volume	116,900 searches tracked	100%
Brand Impressions	2,400 responses mentioning brand	2.0%
Domain Citations	6,000 responses linking to site	5.2%

That 5.2% citation rate applied to 116,900 searches means Superlines is being linked in roughly 6,000 AI responses per month. Even with a conservative 2-5% click-through rate from AI citations, that translates to 120-300 monthly visits from AI search alone — and this traffic has high intent because users are actively researching.

How to use this in reporting

When presenting to stakeholders, frame the metrics as a funnel:

“Our brand appears in 2,400 AI-generated responses per month and is cited with a link in 6,000. Our citation rate of 5.2% is trending upward. Based on our content roadmap, we project improving brand visibility from 1.7% to 3-4% within 60 days, which would increase brand impressions to ~4,300/month.”

This is a concrete business metric that ties content work to measurable outcomes.

A weekly GEO workflow

Once you understand the data and have a content process in place, GEO becomes a weekly routine. Here is a practical 30-minute schedule:

Monday: Health check (10 minutes)

Check Brand Visibility, Citation Rate, and Sentiment in the Overview
Note any metric that moved more than ±1% week-over-week
If sentiment dropped: schedule a content response to reinforce your positioning
If a platform dropped: check which prompts are tracked for that platform

Tuesday: Opportunity review (10 minutes)

Open Opportunities and review new flags
Assign the top Visibility Opportunity and top Competitor Threat to your content calendar
If using the AEO Agent: review its latest report for automatically surfaced opportunities

Wednesday: Mention scan (5 minutes)

Review which prompts generated new brand mentions
Search for 2-3 of the actual AI responses and read the language used
Flag any mischaracterization of your brand for a content correction

Friday: Fan-out query gap check (5 minutes)

Look at the top 10 fan-out queries
For any query gaining citations where you have no URL, add it to your content planning as a confirmed gap
For any query where you rank but weakly (position #5+), add it to your optimization queue

The compounding effect

AI search visibility is not a one-time optimization. It compounds. Every article you publish, every comparison page you update, every third-party mention you earn makes it more likely that AI models will mention and cite you in the future.

The Superlines dashboard gives you the data to see exactly where trust is building and where it is absent. Fan-out queries tell you what the AI is searching for. Citation URLs tell you what it trusts. Opportunities tell you where the biggest gaps are. And trend charts tell you whether your work is moving the needle.

The AEO Agent, if you choose to run it, turns this from a weekly manual process into a daily automated one — surfacing gaps, verifying facts, and producing content drafts that address the exact opportunities your data reveals.

Start with one action: open your dashboard, run through the steps above, and pick a single content brief. That first brief — written, published, and tracked — is how AI search visibility compounds over time.